Machine Learning(ML) is an exciting and fast-paced field. The emerging technology is increasingly becoming the brain behind achieving business intelligence and efficiency.
With its benefits, every industry is looking to apply AI and ML in its domain. As such, more and more companies are on the verge of hiring skilled ML engineers.
ML skills open a world of opportunities for anyone to develop cutting-edge applications. Such skills also provide you with a first-class ticket to some of the most exciting careers in the world today.
But how can you get started in Machine Learning? With numerous ML courses out there, it’s easy to get confused.
I’m a machine learning engineer, but this wasn’t always the case. I started as a bright-eyed electrical engineering student looking for a problem to solve. My ML journey began with an online course, which was followed by more courses and plenty of mentors along the way. It was a wild ride filled with lows and highs, which I wouldn’t change for the world.
Years later, I am a practicing Machine Learning engineer. I am also a founder of a Machine Learning boot camp teaching 1000’s students the basics of machine learning and a weekly AI Scholar Newsletter.
In this article, I attempt to answer the question above by outlining the best ten courses you can take on to help you get started on a career in ML.
Let’s jump into it!
My first experience with machine learning was in Andrew Ng’s Coursera class. Andrew Ng is the CEO/Founder Landing AI; a Coursera co-founder at; Adjunct Professor at Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain.
More than 3.7M students and professionals across the globe have taken this course. With a 4.9 out of 5 rating, it is easy to see why the course is ranked the best on the web.
The course has excellent coverage of supervised and unsupervised algorithms and provides numerous practical insights around implementation to help learners understand ML concepts.
For beginners utterly new in the field, this course is ideal for learning how to create ML algorithms in Python and R and comes with code templates. The course is designed by two professional data scientists who share knowledge to help you learn complex theories, algorithms, and coding libraries in a surprisingly simple way.
“ Efficiently explains most of the essential concepts of ML and I feel lucky to have taken this course. Otherwise, I would still be stuck in my own zone that I couldn’t learn to code” — a student review.
The course is fun and exciting and walks you step-by-step into the world of Machine Learning. With every step and tutorial, the program helps you develop new skills and improve your understanding in the field.
Fastai is dedicated to providing AI education to both beginners and experienced learners. Whether you are getting started or have some experience in the field, you’ll find AI content that is practical-based. The most important thing to note about this course is that it is both free, self-paced, and rated 4.5 out of 5.
Learners use the fast.ai library and train models. You can also join AI forums to communicate with peers and practitioners to help you through the entire learning experience.
This course has nothing on AI and ML as you will notice. Specifically, it gives you easy access to experts’ invaluable learning techniques in many disciplines, including math, science, music, literature, sports, and more. You’ll learn about how the brain uses two very different learning modes and how it encapsulates information which will thus help you on your ML learning journey. The course also covers illusions of learning, memory techniques, dealing with procrastination, and best practices shown by research to be most effective in helping you master tough subjects.
If you want to build intelligent ML applications, this specialization comprises four hands-on courses to help you do that. It is designed by leading researchers at the University of Washington who introduce you to the exciting and high-demand field of Machine Learning.
The course takes you through a series of practical case studies to help you gain applied experience in major areas of ML including, Classification, Prediction, Clustering, and more. It also teaches you to analyze complex datasets and build systems that adapt and improve over time and build intelligent applications that can make predictions from data.
A collaboration between Udacity, Kaggle, and Amazon Web Services (AWS), this course is intended for learners with Python experience but have yet to study ML concepts. It is beautifully crafted to help you learn foundational machine learning algorithms, starting with data cleaning and supervised models. From there, you then move on to exploring deep and unsupervised learning. One of the best things about this course is that you get the necessary practical experience by applying your skills to coding exercises and projects at every step.
By the end of the course, you’ll be ready to start working with a number of essential ML algorithms. If you are interested in ML, I would highly recommend this course as it will surpass your expectations. To ensure success and a smooth journey in the program, course designers have recommended that learners have basic python programming, statistics, and probability skills.
With a rating of 4.6 out of 5, this course is specially designed for students already armed with the knowledge of machine learning algorithms because it teaches advanced machine learning techniques and algorithms. Specifically, the program will teach you how to package and deploy your ML models to a production environment.
By the end of the course, you will gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate your models’ performance. You will also learn how to A/B test models and update them as you gather more data, which is crucial in the industry.
This is a four-course hands-on program that teaches you the necessary tools to build scalable AI-powered applications with TensorFlow. TensorFlow is a core open-source library for developing and training ML models. It is, therefore, one of the most in-demand and popular open-source deep learning frameworks available today.
So you learn applied machine learning skills with TensorFlow so you can build and train powerful models. And after finishing the course, you’ll be able to use your new TensorFlow skills to a wide range of problems and projects.
As highlighted in the intro, AI is transforming every industry across the globe. This is a course by Andrew Ng that you can take after completing the Machine Learning course. Specifically, the specialization has five courses that help you master Deep Learning foundations and concepts, understand how to apply them, and build an AI career after completing the ML course.
You also understand how to create neural networks and perform successful machine learning projects. You will learn about CNNs, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You get to work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. That means mastering not only theoretical concepts but understanding how they are applied in the industry. You will practice the concepts in Python and TensorFlow. What more, you’ll also get to hear from several top leaders in Deep Learning, who will share with you their personal stories and give you career advice in the course.
Numerous online courses can give you useful knowledge and skills in the ML field. If you’ve seen a program that interests you, go ahead and sign up. Do it now; it’s worth it! Go through the course and arm yourself with the knowledge and put what you are learning to practice by doing projects.
And if you have recommendations for other courses that you think are great, please share them with me in the comments sections.
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